Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Propagation of Action Potentials

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium...

State Space Representation

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...

Mechanistic Models: Overview of Compartment Models

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...

State Space to Transfer Function

State Space to Transfer Function

The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:

Pole and System Stability

Pole and System Stability

The transfer function is a fundamental concept representing the ratio of two polynomials. The numerator and denominator encapsulate the system's dynamics. The zeros and poles of this transfer function are critical in determining the system's behavior and stability.
Simple poles are unique roots of the denominator polynomial. Each simple pole corresponds to a distinct solution to the system's characteristic equation, typically resulting in exponential decay terms in the system's...

First Law: Particles in Two-dimensional Equilibrium

First Law: Particles in Two-dimensional Equilibrium

Recall that a particle in equilibrium is one for which the external forces are balanced. Static equilibrium involves objects at rest, and dynamic equilibrium involves objects in motion without acceleration; but it is important to remember that these conditions are relative. For instance, an object may be at rest when viewed from one frame of reference, but that same object would appear to be in motion when viewed by someone moving at a constant velocity.
Newton's first law tells us about...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Spiral, target, stripe, and disordered waves in active six-state Potts models.

The Journal of chemical physics·2026

Same author

Coarsening dynamics for spiral and disordered waves in active Potts models.

Physical review. E·2026

Same author

Familial hypomagnesemia with hypercalciuria and nephrocalcinosis treated with living-donor kidney transplantation: case report.

CEN case reports·2026

Same author

Identification of Urinary Exosomal microRNAs Associated With Subclinical Acute T-Cell-Mediated Rejection in Kidney Transplant Recipients.

Clinical and translational science·2026

Same author

Dynamics due to competitive flip cycles in active Potts models.

Physical review. E·2026

Same author

Reliability of Preoperative MRI Findings for Differentiating Spontaneous Spinal Subdural and Epidural Hematomas: A Multi-Institutional Retrospective Study of 27 Surgically Treated Cases.

Journal of clinical medicine·2026

Search research articles

Related Experiment Video

Updated: May 7, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Spatiotemporal patterns in active four-state Potts models.

Hiroshi Noguchi¹

¹Institute for Solid State Physics, University of Tokyo, Kashiwa, Chiba, 277-8581, Japan. noguchi@issp.u-tokyo.ac.jp.

Scientific Reports

|January 3, 2025

Summary

This study reveals steady, long-term spatiotemporal patterns in cyclic Potts models, moving beyond transient dynamics. Nucleation and growth are key to observing cyclic phase changes and spatial coexistence.

More Related Videos

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior

Published on: December 28, 2010

Related Experiment Videos

Last Updated: May 7, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior

Published on: December 28, 2010

Area of Science:

Complex Systems
Statistical Physics
Non-equilibrium Dynamics

Background:

Spatiotemporal patterns are common in non-equilibrium systems.
Previous models focused on transient dynamics, with systems eventually reaching a single stable phase.

Purpose of the Study:

To investigate steady, long-term spatiotemporal dynamics in cyclic Potts models.
To explore the role of nucleation and growth in pattern formation.
To analyze pattern behavior under symmetric and asymmetric cyclic conditions.

Main Methods:

Utilized cyclic Potts models to simulate system dynamics.
Varied flipping energies to observe pattern evolution.
Introduced asymmetric conditions and three-state cycling for comparative analysis.

Main Results:

Achieved steady long-term dynamics, unlike previous transient models.
Observed cyclic phase changes and spatial coexistence of four phases under symmetric conditions.
Identified spatial coexistence of two diagonal phases and shrinking circular domains under asymmetric conditions.

Conclusions:

Cyclic Potts models can exhibit stable, long-term spatiotemporal patterns.
Nucleation and growth are critical mechanisms driving these patterns.
System symmetry and cycling complexity significantly influence emergent dynamics.